9, 10 Texture and histogram features based on MR images have potential to provide noninvasive imaging biomarkers to aid in breast cancer diagnosis, prognosis and treatment response evaluation.

7, 8 With the advances in computer technology, extracting large data from medical images using automatic algorithms becomes more feasible and вЂњradiomicsвЂќ, which allows high-throughput extraction of tremendous amount of quantitative information from radiographic images, emerged. 4вЂ“ 8 In addition to providing quantitative parameters related to shape, internal heterogeneity and DCE kinetics, the CAD features were further related to BI-RADS descriptors, 5, 6 and used to build separate diagnostic models for mass and non-mass-like enhancements, respectively. This problem was well recognized, and many computer-aided-diagnosis (CAD) methods have been developed and reported in the literature in the last two decades. With increasing screening and preoperative MRI performed, particularly in community settings, 3 an efficient way for characterization of the enhancing lesions is important to improve diagnostic accuracy.Д¬onventional diagnosis made by radiologists is mainly based on evaluation of the morphological features and the DCE time course, which is subjective and varies with radiologistsвЂ™ experience. 1, 2 Many benign lesions also show strong contrast enhancement, and may lead to false positive diagnosis, unnecessary biopsy or over treatment. Breast MRI is an important imaging modality for screening, diagnosis and pre-operative staging of breast cancer.

Cancer dataset in matlab 2012